A stochastic L-BFGS approach for full-waveform inversion
Bibliographic record
Abstract
Speeding-up convergence rates and reducing the computational burden of Full Waveform Inversion (FWI) is increasingly important as we move toward large-scale 3D multi-parameter inversion. To this end, second-order optimization algorithms like L-BFGS or the truncated Newton method allow a much faster convergence rate at minimal computational costs. In the same fashion, stochastic source subsampling approaches have been shown to reduce the computational cost of FWI. In this study, we propose to combine these two strategies and present how the L-BFGS algorithm can be used along with the stochastic source subsampling strategy, or what we call the stochastic L-BFGS algorithm. Presentation Date: Tuesday, September 26, 2017 Start Time: 2:40 PM Location: Exhibit Hall C, E-P Station 3 Presentation Type: EPOSTER
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".